Nelder-Mead implementation in Python

Question

I am working on a non-linear optimization problem, containing bounds and constraints for the variables. Very complex problem involving networks and logical functions. I have been switching a tool from the Matlab language in which I originally implemented it, to Python. In the Matlab version, I used the 'interior-point' method of estimating the Hessian to perform gradient descent. I tested several options for the function scipy.minimize(), and the best result is obtained for the Nedler-Mead method. I read the paper but it seems that this method cannot incorporate bounds and constraints directly. I would like to understand exactly what it does with my constraints before I set up to use this method in the research project. Thanks in advance